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PyData London News April 2023¶

73rd Meetup, hosted by MAN Group¶

@PyDataLondon¶

PyData Code of Conduct¶

https://pydata.org/code-of-conduct/¶

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NumFOCUS¶

  • US-based charity in support of open-source scientific computing
  • Financial and admin support for many open-source projects
  • Support for many community events
  • https://numfocus.org
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PyData¶

  • Educational program by NumFOCUS
  • A global community
  • 216 groups across 74 countries world-wide
  • 210,000+ users world-wide
  • https://www.meetup.com/pro/pydata/
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PyData London¶

  • 12,110+ members, ~5%
  • Monthly meetup and annual conference
  • All run by volunteers
  • https://london.pydata.org/
  • Propose a Talk: https://london.pydata.org/submit-a-talk/
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Upcoming Conferences¶

🇺🇸 April 17th - 19th PyCon DE & PyData Berlin 2023¶

🇺🇸 April 26th - 28th PyData Seattle 2023¶

🇺🇸 May 10th - 12th JupyterCon 2023¶

🇺🇸 May 17th - 12th PyCon Lithuania 2023¶

Upcoming Local Meetups & Events¶

🐍 Tue 4th April = ⭐ PyData London (73rd Meetup) ⭐¶

🐍 Thursday 27th April = PyData Bristol¶

Socials!¶

Come join the community on on Slack, Twitter or Linkedin

PyData UK Community Slack channel¶

Come join the community on our UK-wide Slack channel!

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@pydatalondon, twitter¶

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Linkedin¶

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PyData London CFP¶

  • CfP closed on time, with ~200 proposals from ~170 people
  • Each proposal has received ~4-5 reviews, double-blind
  • Powered by https://pretalx.com
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QR code to the PyData London CFP¶

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PyData London Conference¶

  • Tickets on sale!
  • Sponsorship open!
  • June 2nd - 4th
  • https://pydata.org/london2023
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Tonight's Schedule¶

18:30 Doors open¶

19:00 Talks start ⬅️ YOU ARE HERE¶

19:15 Talk #1 + Q&A¶

19:45 Break 🥂¶

20:00 Talk #2 + Q&A¶

20:30 ⚡ Lightning Talks ⚡ + 📣 Community Announcements 📣¶

20:45 to 21:00 We're done 🔜 The Banker 🍻¶

ArcticDB Adventures - Abhi Thakur¶

The journey of building a public-facing and low-maintenance Wordle Solver - Jason Chao¶

This talk is about my journey of building a public-facing Wordle Solver meant to be future-proof. It showcases the possibility of rapidly developing a low-maintenance and public-facing side project with the help of unsupervised learning.

The Wordle game captivates human players and people who want to build machine players. The developers of most Wordle solvers strive to find the best or optimal solution to the game by using supervised learning targeting a specific word list. However, these approaches may take a long time to train a model and easily overfit / only work well with a specific Wordle implementation.

Despite time constraints, I built a Wordle Solver that works with multiple Wordle versions and supports variable word lengths. Moreover, the World Solver is now running with almost zero maintenance and at a low operating cost.

⚡ Lightning Talks ⚡¶

Python can send the emails for me - controlling outlook from Python - Tambe Tabitha Achere¶

Down the R rabbit hole -- how I picked up python tricks by converting R code to python - Wendy Mak¶